Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations1028487
Missing cells0
Missing cells (%)0.0%
Duplicate rows9158
Duplicate rows (%)0.9%
Total size in memory86.3 MiB
Average record size in memory88.0 B

Variable types

Numeric10

Alerts

Dataset has 9158 (0.9%) duplicate rowsDuplicates
Depth (m) is highly overall correlated with u0 (kPa) and 2 other fieldsHigh correlation
Fr (%) is highly overall correlated with Qtn (-) and 2 other fieldsHigh correlation
Qtn (-) is highly overall correlated with Fr (%) and 2 other fieldsHigh correlation
Rf (%) is highly overall correlated with Fr (%) and 1 other fieldsHigh correlation
fs (kPa) is highly overall correlated with Qtn (-) and 1 other fieldsHigh correlation
qc (MPa) is highly overall correlated with Fr (%) and 3 other fieldsHigh correlation
u0 (kPa) is highly overall correlated with Depth (m) and 2 other fieldsHigh correlation
σ',v (kPa) is highly overall correlated with Depth (m) and 2 other fieldsHigh correlation
σ,v (kPa) is highly overall correlated with Depth (m) and 2 other fieldsHigh correlation
Rf (%) is highly skewed (γ1 = 464.7144979) Skewed
Fr (%) is highly skewed (γ1 = 264.9607272) Skewed
Rf (%) has 11801 (1.1%) zeros Zeros
u0 (kPa) has 31650 (3.1%) zeros Zeros
Fr (%) has 11683 (1.1%) zeros Zeros
Oberhollenzer_classes has 78436 (7.6%) zeros Zeros

Reproduction

Analysis started2024-11-07 15:02:57.538508
Analysis finished2024-11-07 15:04:55.809756
Duration1 minute and 58.27 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Depth (m)
Real number (ℝ)

High correlation 

Distinct7553
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.226225
Minimum0.01
Maximum75.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 MiB
2024-11-07T10:04:56.192411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile1.07
Q15.32
median11.06
Q318.07
95-th percentile34.74
Maximum75.92
Range75.91
Interquartile range (IQR)12.75

Descriptive statistics

Standard deviation10.565445
Coefficient of variation (CV)0.79882543
Kurtosis2.8156587
Mean13.226225
Median Absolute Deviation (MAD)6.25
Skewness1.430253
Sum13603000
Variance111.62862
MonotonicityNot monotonic
2024-11-07T10:04:56.667972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.27 488
 
< 0.1%
3.23 488
 
< 0.1%
3.24 488
 
< 0.1%
2.22 488
 
< 0.1%
2.24 488
 
< 0.1%
2.2 488
 
< 0.1%
2.29 488
 
< 0.1%
2.28 488
 
< 0.1%
2.14 488
 
< 0.1%
2.17 488
 
< 0.1%
Other values (7543) 1023607
99.5%
ValueCountFrequency (%)
0.01 461
< 0.1%
0.02 467
< 0.1%
0.03 472
< 0.1%
0.04 476
< 0.1%
0.05 479
< 0.1%
0.06 480
< 0.1%
0.07 481
< 0.1%
0.08 482
< 0.1%
0.09 481
< 0.1%
0.1 477
< 0.1%
ValueCountFrequency (%)
75.92 1
< 0.1%
75.91 1
< 0.1%
75.9 1
< 0.1%
75.89 1
< 0.1%
75.88 1
< 0.1%
75.87 1
< 0.1%
75.86 1
< 0.1%
75.85 1
< 0.1%
75.84 1
< 0.1%
75.83 1
< 0.1%

qc (MPa)
Real number (ℝ)

High correlation 

Distinct7190
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3297195
Minimum-1.37
Maximum101.73
Zeros4667
Zeros (%)0.5%
Negative4279
Negative (%)0.4%
Memory size15.7 MiB
2024-11-07T10:04:57.085121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1.37
5-th percentile0.31
Q10.96
median2.3
Q35.98
95-th percentile21.51
Maximum101.73
Range103.1
Interquartile range (IQR)5.02

Descriptive statistics

Standard deviation8.3905404
Coefficient of variation (CV)1.574293
Kurtosis16.271306
Mean5.3297195
Median Absolute Deviation (MAD)1.68
Skewness3.5921414
Sum5481547.2
Variance70.401168
MonotonicityNot monotonic
2024-11-07T10:04:57.485684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4667
 
0.5%
0.92 4446
 
0.4%
0.94 4201
 
0.4%
0.91 4134
 
0.4%
0.93 4063
 
0.4%
0.9 4041
 
0.4%
0.95 3971
 
0.4%
0.85 3943
 
0.4%
1 3906
 
0.4%
0.97 3893
 
0.4%
Other values (7180) 987222
96.0%
ValueCountFrequency (%)
-1.37 5
 
< 0.1%
-0.2 1
 
< 0.1%
-0.19 14
 
< 0.1%
-0.18 32
 
< 0.1%
-0.17 14
 
< 0.1%
-0.16 8
 
< 0.1%
-0.15 12
 
< 0.1%
-0.14 10
 
< 0.1%
-0.13 73
< 0.1%
-0.12 97
< 0.1%
ValueCountFrequency (%)
101.73 1
< 0.1%
101.1 1
< 0.1%
101.02 1
< 0.1%
97.08 1
< 0.1%
95.84 1
< 0.1%
95.51 1
< 0.1%
95.2 1
< 0.1%
94.94 1
< 0.1%
94.89 1
< 0.1%
94.78 1
< 0.1%

fs (kPa)
Real number (ℝ)

High correlation 

Distinct10296
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.605697
Minimum-99.9
Maximum1591.4
Zeros8576
Zeros (%)0.8%
Negative8794
Negative (%)0.9%
Memory size15.7 MiB
2024-11-07T10:04:57.823741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-99.9
5-th percentile4.6
Q116.2
median32.5
Q366
95-th percentile175.8
Maximum1591.4
Range1691.3
Interquartile range (IQR)49.8

Descriptive statistics

Standard deviation70.295035
Coefficient of variation (CV)1.2873205
Kurtosis24.72372
Mean54.605697
Median Absolute Deviation (MAD)20.1
Skewness3.9766361
Sum56161250
Variance4941.3919
MonotonicityNot monotonic
2024-11-07T10:04:58.199696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8576
 
0.8%
15.9 2286
 
0.2%
11 2281
 
0.2%
14.3 2243
 
0.2%
10.9 2196
 
0.2%
12.1 2194
 
0.2%
12.4 2188
 
0.2%
12.6 2187
 
0.2%
13.2 2164
 
0.2%
14.8 2164
 
0.2%
Other values (10286) 1000008
97.2%
ValueCountFrequency (%)
-99.9 1
< 0.1%
-99.8 1
< 0.1%
-99.7 1
< 0.1%
-98.7 1
< 0.1%
-98.4 2
< 0.1%
-98.1 1
< 0.1%
-98 1
< 0.1%
-97.7 1
< 0.1%
-97.4 1
< 0.1%
-97.3 1
< 0.1%
ValueCountFrequency (%)
1591.4 1
 
< 0.1%
1474 1
 
< 0.1%
1389.6 1
 
< 0.1%
1191.2 2
< 0.1%
1160.7 1
 
< 0.1%
1154.8 2
< 0.1%
1150.5 2
< 0.1%
1118.5 3
< 0.1%
1109.7 2
< 0.1%
1098.7 2
< 0.1%

Rf (%)
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct6749
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3947058
Minimum-100
Maximum22000
Zeros11801
Zeros (%)1.1%
Negative11823
Negative (%)1.1%
Memory size15.7 MiB
2024-11-07T10:04:58.624085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile0.19
Q10.69
median1.37
Q32.48
95-th percentile6.57
Maximum22000
Range22100
Interquartile range (IQR)1.79

Descriptive statistics

Standard deviation35.100875
Coefficient of variation (CV)14.657698
Kurtosis258076.02
Mean2.3947058
Median Absolute Deviation (MAD)0.81
Skewness464.7145
Sum2462923.8
Variance1232.0714
MonotonicityNot monotonic
2024-11-07T10:04:59.147124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11801
 
1.1%
0.4 4556
 
0.4%
0.36 4543
 
0.4%
0.39 4526
 
0.4%
0.41 4455
 
0.4%
0.34 4445
 
0.4%
0.37 4438
 
0.4%
0.33 4401
 
0.4%
0.42 4373
 
0.4%
0.35 4368
 
0.4%
Other values (6739) 976581
95.0%
ValueCountFrequency (%)
-100 10
< 0.1%
-98.95 1
 
< 0.1%
-98.82 1
 
< 0.1%
-98.77 1
 
< 0.1%
-98.66 1
 
< 0.1%
-98.55 1
 
< 0.1%
-97.66 1
 
< 0.1%
-97.3 10
< 0.1%
-97.17 1
 
< 0.1%
-97.05 1
 
< 0.1%
ValueCountFrequency (%)
22000 1
< 0.1%
19333.33 1
< 0.1%
12200 1
< 0.1%
8900 1
< 0.1%
3906.58 1
< 0.1%
3284.09 1
< 0.1%
3009.87 1
< 0.1%
2822.63 1
< 0.1%
2425.93 1
< 0.1%
2400 2
< 0.1%

σ,v (kPa)
Real number (ℝ)

High correlation 

Distinct8356
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean251.29829
Minimum0.19
Maximum1442.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 MiB
2024-11-07T10:04:59.816040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.19
5-th percentile20.33
Q1101.08
median210.14
Q3343.33
95-th percentile660.06
Maximum1442.48
Range1442.29
Interquartile range (IQR)242.25

Descriptive statistics

Standard deviation200.74348
Coefficient of variation (CV)0.79882547
Kurtosis2.8156558
Mean251.29829
Median Absolute Deviation (MAD)118.75
Skewness1.4302526
Sum2.5845703 × 108
Variance40297.944
MonotonicityNot monotonic
2024-11-07T10:05:00.192213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.37 488
 
< 0.1%
61.56 488
 
< 0.1%
60.42 488
 
< 0.1%
60.8 488
 
< 0.1%
40.47 488
 
< 0.1%
60.04 488
 
< 0.1%
60.61 488
 
< 0.1%
62.32 488
 
< 0.1%
59.85 488
 
< 0.1%
63.65 488
 
< 0.1%
Other values (8346) 1023607
99.5%
ValueCountFrequency (%)
0.19 461
< 0.1%
0.38 467
< 0.1%
0.57 472
< 0.1%
0.76 476
< 0.1%
0.95 479
< 0.1%
1.14 480
< 0.1%
1.33 481
< 0.1%
1.52 482
< 0.1%
1.71 481
< 0.1%
1.9 477
< 0.1%
ValueCountFrequency (%)
1442.48 1
< 0.1%
1442.29 1
< 0.1%
1442.1 1
< 0.1%
1441.91 1
< 0.1%
1441.72 1
< 0.1%
1441.53 1
< 0.1%
1441.34 1
< 0.1%
1441.15 1
< 0.1%
1440.96 1
< 0.1%
1440.77 1
< 0.1%

u0 (kPa)
Real number (ℝ)

High correlation  Zeros 

Distinct8378
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.63838
Minimum0
Maximum744.78
Zeros31650
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size15.7 MiB
2024-11-07T10:05:00.596778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.12
Q145.71
median101.14
Q3170.11
95-th percentile330.89
Maximum744.78
Range744.78
Interquartile range (IQR)124.4

Descriptive statistics

Standard deviation103.13878
Coefficient of variation (CV)0.84099926
Kurtosis3.0591775
Mean122.63838
Median Absolute Deviation (MAD)60.72
Skewness1.4734031
Sum1.2613198 × 108
Variance10637.609
MonotonicityNot monotonic
2024-11-07T10:05:00.970163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31650
 
3.1%
7.55 489
 
< 0.1%
7.36 488
 
< 0.1%
12.26 488
 
< 0.1%
7.75 488
 
< 0.1%
12.36 488
 
< 0.1%
3.73 488
 
< 0.1%
7.65 488
 
< 0.1%
12.46 488
 
< 0.1%
29.14 487
 
< 0.1%
Other values (8368) 992445
96.5%
ValueCountFrequency (%)
0 31650
3.1%
0.1 466
 
< 0.1%
0.2 471
 
< 0.1%
0.29 475
 
< 0.1%
0.39 478
 
< 0.1%
0.49 481
 
< 0.1%
0.59 483
 
< 0.1%
0.69 484
 
< 0.1%
0.78 484
 
< 0.1%
0.88 483
 
< 0.1%
ValueCountFrequency (%)
744.78 1
< 0.1%
744.68 1
< 0.1%
744.58 1
< 0.1%
744.48 1
< 0.1%
744.38 1
< 0.1%
744.28 1
< 0.1%
744.19 1
< 0.1%
744.09 1
< 0.1%
743.99 1
< 0.1%
743.89 1
< 0.1%

σ',v (kPa)
Real number (ℝ)

High correlation 

Distinct43101
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.66001
Minimum0.09
Maximum697.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 MiB
2024-11-07T10:05:01.488623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.09
5-th percentile11.12
Q154.13
median108.9
Q3174.7
95-th percentile330.2
Maximum697.7
Range697.61
Interquartile range (IQR)120.57

Descriptive statistics

Standard deviation99.581525
Coefficient of variation (CV)0.77398974
Kurtosis2.3720283
Mean128.66001
Median Absolute Deviation (MAD)59.3
Skewness1.3359932
Sum1.3232514 × 108
Variance9916.48
MonotonicityNot monotonic
2024-11-07T10:05:01.993852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.4 482
 
< 0.1%
5.7 482
 
< 0.1%
16.91 482
 
< 0.1%
11.21 481
 
< 0.1%
8.55 481
 
< 0.1%
5.51 481
 
< 0.1%
14.06 481
 
< 0.1%
8.36 479
 
< 0.1%
2.85 478
 
< 0.1%
19.76 477
 
< 0.1%
Other values (43091) 1023683
99.5%
ValueCountFrequency (%)
0.09 355
< 0.1%
0.18 360
< 0.1%
0.19 106
 
< 0.1%
0.28 364
< 0.1%
0.37 367
< 0.1%
0.38 107
 
< 0.1%
0.46 370
< 0.1%
0.55 372
< 0.1%
0.57 108
 
< 0.1%
0.64 373
< 0.1%
ValueCountFrequency (%)
697.7 1
< 0.1%
697.61 1
< 0.1%
697.52 1
< 0.1%
697.43 1
< 0.1%
697.34 1
< 0.1%
697.25 1
< 0.1%
697.15 1
< 0.1%
697.06 1
< 0.1%
696.97 1
< 0.1%
696.88 1
< 0.1%

Qtn (-)
Real number (ℝ)

High correlation 

Distinct58724
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.936159
Minimum-127.57
Maximum1001
Zeros12
Zeros (%)< 0.1%
Negative13556
Negative (%)1.3%
Memory size15.7 MiB
2024-11-07T10:05:02.583715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-127.57
5-th percentile1.69
Q15.19
median22.47
Q360.56
95-th percentile289.56
Maximum1001
Range1128.57
Interquartile range (IQR)55.37

Descriptive statistics

Standard deviation118.64492
Coefficient of variation (CV)1.885163
Kurtosis19.483707
Mean62.936159
Median Absolute Deviation (MAD)19.19
Skewness3.9490944
Sum64729021
Variance14076.616
MonotonicityNot monotonic
2024-11-07T10:05:03.067512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001 1961
 
0.2%
1.98 959
 
0.1%
1.97 956
 
0.1%
1.91 948
 
0.1%
1.99 943
 
0.1%
2.03 933
 
0.1%
1.89 917
 
0.1%
1.88 917
 
0.1%
1.9 913
 
0.1%
2.04 898
 
0.1%
Other values (58714) 1018142
99.0%
ValueCountFrequency (%)
-127.57 1
< 0.1%
-84.77 1
< 0.1%
-80.78 1
< 0.1%
-76.06 1
< 0.1%
-68.47 1
< 0.1%
-58.36 1
< 0.1%
-55.14 1
< 0.1%
-53.94 1
< 0.1%
-53.21 1
< 0.1%
-52.48 1
< 0.1%
ValueCountFrequency (%)
1001 1961
0.2%
999.79 2
 
< 0.1%
999.51 1
 
< 0.1%
999.48 1
 
< 0.1%
999.27 1
 
< 0.1%
999.09 1
 
< 0.1%
999.03 1
 
< 0.1%
998.99 1
 
< 0.1%
998.87 1
 
< 0.1%
998.81 1
 
< 0.1%

Fr (%)
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct10389
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5622795
Minimum-100
Maximum33166.67
Zeros11683
Zeros (%)1.1%
Negative14264
Negative (%)1.4%
Memory size15.7 MiB
2024-11-07T10:05:03.461807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile0.19
Q10.75
median1.68
Q33.34
95-th percentile8.41
Maximum33166.67
Range33266.67
Interquartile range (IQR)2.59

Descriptive statistics

Standard deviation81.371941
Coefficient of variation (CV)22.842661
Kurtosis85744.035
Mean3.5622795
Median Absolute Deviation (MAD)1.13
Skewness264.96073
Sum3663758.1
Variance6621.3927
MonotonicityNot monotonic
2024-11-07T10:05:03.836382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11683
 
1.1%
0.37 4445
 
0.4%
0.33 4281
 
0.4%
0.36 4272
 
0.4%
0.38 4183
 
0.4%
0.34 4177
 
0.4%
0.4 4100
 
0.4%
0.35 4083
 
0.4%
0.39 4074
 
0.4%
0.43 4072
 
0.4%
Other values (10379) 979117
95.2%
ValueCountFrequency (%)
-100 2
< 0.1%
-99.95 1
< 0.1%
-99.48 1
< 0.1%
-99.34 1
< 0.1%
-99.28 1
< 0.1%
-99.26 1
< 0.1%
-99.04 1
< 0.1%
-98.96 1
< 0.1%
-98.84 1
< 0.1%
-98.83 1
< 0.1%
ValueCountFrequency (%)
33166.67 1
< 0.1%
31777.78 1
< 0.1%
27500 1
< 0.1%
26000 1
< 0.1%
20260.87 1
< 0.1%
18000 1
< 0.1%
17162.79 1
< 0.1%
16400 1
< 0.1%
14113.21 1
< 0.1%
13777.78 1
< 0.1%

Oberhollenzer_classes
Real number (ℝ)

Zeros 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0545257
Minimum0
Maximum7
Zeros78436
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size15.7 MiB
2024-11-07T10:05:04.165937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2756955
Coefficient of variation (CV)0.56127293
Kurtosis-1.2471513
Mean4.0545257
Median Absolute Deviation (MAD)2
Skewness-0.33405877
Sum4170027
Variance5.1787902
MonotonicityNot monotonic
2024-11-07T10:05:04.466091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 208247
20.2%
2 192904
18.8%
6 180407
17.5%
7 163684
15.9%
1 99211
9.6%
4 98749
9.6%
0 78436
 
7.6%
3 6849
 
0.7%
ValueCountFrequency (%)
0 78436
 
7.6%
1 99211
9.6%
2 192904
18.8%
3 6849
 
0.7%
4 98749
9.6%
5 208247
20.2%
6 180407
17.5%
7 163684
15.9%
ValueCountFrequency (%)
7 163684
15.9%
6 180407
17.5%
5 208247
20.2%
4 98749
9.6%
3 6849
 
0.7%
2 192904
18.8%
1 99211
9.6%
0 78436
 
7.6%

Interactions

2024-11-07T10:04:38.416426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:27.074931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:34.623747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:42.486206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:50.840830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:01.430809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:09.833771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:16.956944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:23.682830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:29.552620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:39.874743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:27.812541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:35.225691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:43.115319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:52.453916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:02.233121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:10.768121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:17.505035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:24.248355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:30.532241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:41.000056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:28.527848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:35.864649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:43.752985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:53.747424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:03.239643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:11.482080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:18.001200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:24.815442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:31.263204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:42.161867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:29.146401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:36.631585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:44.400538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:54.565867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:04.983106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:12.352065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:18.599005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:25.324214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:32.077697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:43.307088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:29.653474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:37.249063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:45.044625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:55.575494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:05.700938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:13.019720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:19.205770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:25.845519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:32.672250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:44.445589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:30.305190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:38.058973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:45.630548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:56.347296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:06.301351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:13.684732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:19.886439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:26.370821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:33.308213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:45.911286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:31.003582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:38.722601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:46.183480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:57.131620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:06.891225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:14.230643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:20.814129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:26.924845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:34.666848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:47.035194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:31.690605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:39.442817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:46.851588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:58.448436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:07.468692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:14.814062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:21.385479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:27.415412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:35.326921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:48.277467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:32.511835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:40.223640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:47.393869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:59.125295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:08.005548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:15.338399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:21.969619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:27.930645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:35.846622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:49.871703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:33.890736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:41.819058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:03:48.807491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:00.527166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:09.125055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:16.441309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:23.116062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:29.059695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T10:04:36.977591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-07T10:05:04.704365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Depth (m)Fr (%)Oberhollenzer_classesQtn (-)Rf (%)fs (kPa)qc (MPa)u0 (kPa)σ',v (kPa)σ,v (kPa)
Depth (m)1.0000.0720.264-0.461-0.048-0.0000.0550.9830.9861.000
Fr (%)0.0721.0000.260-0.5490.9440.091-0.6240.0790.0650.072
Oberhollenzer_classes0.2640.2601.000-0.3860.187-0.203-0.2950.2510.2670.264
Qtn (-)-0.461-0.549-0.3861.000-0.4170.5530.818-0.451-0.457-0.461
Rf (%)-0.0480.9440.187-0.4171.0000.204-0.547-0.039-0.055-0.048
fs (kPa)-0.0000.091-0.2030.5530.2041.0000.639-0.0070.006-0.000
qc (MPa)0.055-0.624-0.2950.818-0.5470.6391.0000.0430.0640.055
u0 (kPa)0.9830.0790.251-0.451-0.039-0.0070.0431.0000.9400.983
σ',v (kPa)0.9860.0650.267-0.457-0.0550.0060.0640.9401.0000.986
σ,v (kPa)1.0000.0720.264-0.461-0.048-0.0000.0550.9830.9861.000

Missing values

2024-11-07T10:04:50.293916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-07T10:04:51.793046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Depth (m)qc (MPa)fs (kPa)Rf (%)σ,v (kPa)u0 (kPa)σ',v (kPa)Qtn (-)Fr (%)Oberhollenzer_classes
14865100.010.000.000.000.190.100.09-27.090.004.0
14865110.020.000.000.000.380.200.18-14.580.004.0
14865120.030.000.000.000.570.290.28-10.410.004.0
14865130.040.000.000.000.760.390.37-8.320.004.0
14865140.050.000.000.000.950.490.46-7.070.004.0
14865150.060.000.00-0.011.140.590.55-6.240.004.0
14865160.070.000.00-0.011.330.690.64-5.64-0.014.0
14865170.080.000.00-0.011.520.780.74-5.20-0.014.0
14865180.090.000.00-0.011.710.880.83-4.85-0.014.0
14865190.100.000.00-0.021.900.980.92-4.57-0.014.0
Depth (m)qc (MPa)fs (kPa)Rf (%)σ,v (kPa)u0 (kPa)σ',v (kPa)Qtn (-)Fr (%)Oberhollenzer_classes
25169699.7744.1779.900.22185.6371.32114.31409.960.221.0
25169709.7844.37105.500.14185.8271.42114.40427.650.141.0
25169719.7946.080.000.08186.0171.51114.50440.750.081.0
25169729.8047.790.000.00186.2071.61114.59413.800.001.0
25169739.8148.940.000.00186.3971.71114.68423.670.001.0
25169749.8249.590.000.00186.5871.81114.77431.220.001.0
25169759.8350.510.000.00186.7771.91114.86446.310.001.0
25169769.8454.250.000.00186.9672.01114.95461.360.001.0
25169779.8554.910.000.00187.1572.10115.05473.500.001.0
25169789.8654.830.000.00187.3472.20115.14474.780.001.0

Duplicate rows

Most frequently occurring

Depth (m)qc (MPa)fs (kPa)Rf (%)σ,v (kPa)u0 (kPa)σ',v (kPa)Qtn (-)Fr (%)Oberhollenzer_classes# duplicates
280.010.340.000.000.190.100.091001.000.000.05
110.010.090.000.000.190.100.091001.000.000.04
130.010.120.000.000.190.100.091001.000.000.04
1040.040.480.000.000.760.390.371001.000.000.04
00.010.000.000.000.190.100.09-44.500.000.03
50.010.000.000.000.190.100.096.640.000.03
190.010.230.000.000.190.100.091001.000.000.03
210.010.280.000.000.190.100.091001.000.000.03
390.010.630.000.000.190.100.091001.000.000.03
460.020.000.000.000.380.200.182.290.000.03